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Journal articles on the topic 'Statistical computation'

Author: Grafiati

Published: 4 June 2021

Last updated: 17 June 2025

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1

Kemp, A. W., and J. H. Maindonald. "Statistical Computation." Biometrics 42, no. 4 (1986): 1004. http://dx.doi.org/10.2307/2530723.

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2

Heller, Barbara. "Statistical computation." Mathematical Modelling 7, no. 9-12 (1986): 1658–59. http://dx.doi.org/10.1016/0270-0255(86)90103-x.

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3

Nelson, Lloyd S. "Statistical Computation." Journal of Quality Technology 18, no. 4 (1986): 259. http://dx.doi.org/10.1080/00224065.1986.11979022.

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4

Weisberg, Sanford, and J. H. Maindonald. "Statistical Computation." Journal of the American Statistical Association 80, no. 392 (1985): 1081. http://dx.doi.org/10.2307/2288606.

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5

Robert, Christian. "Statistical Modeling and Computation." CHANCE 27, no. 2 (2014): 61–62. http://dx.doi.org/10.1080/09332480.2014.914766.

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6

Hemmo, Meir, and Orly Shenker. "The Multiple-Computations Theorem and the Physics of Singling Out a Computation." Monist 105, no. 2 (2022): 175–93. http://dx.doi.org/10.1093/monist/onab030.

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Abstract The problem of multiple-computations discovered by Hilary Putnam presents a deep difficulty for functionalism (of all sorts, computational and causal). We describe in outline why Putnam’s result, and likewise the more restricted result we call the Multiple-Computations Theorem, are in fact theorems of statistical mechanics. We show why the mere interaction of a computing system with its environment cannot single out a computation as the preferred one amongst the many computations implemented by the system. We explain why nonreductive approaches to solving the multiple-computations problem, and in particular why computational externalism, are dualistic in the sense that they imply that nonphysical facts in the environment of a computing system single out the computation. We discuss certain attempts to dissolve Putnam’s unrestricted result by appealing to systems with certain kinds of input and output states as a special case of computational externalism, and show why this approach is not workable without collapsing to behaviorism. We conclude with some remarks about the nonphysical nature of mainstream approaches to both statistical mechanics and the quantum theory of measurement with respect to the singling out of partitions and observables.

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7

Ordonez, Carlos. "Statistical Model Computation with UDFs." IEEE Transactions on Knowledge and Data Engineering 22, no. 12 (2010): 1752–65. http://dx.doi.org/10.1109/tkde.2010.44.

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8

Hertz, J. A. "Statistical Mechanics of Neural Computation." International Journal of Supercomputing Applications 2, no. 4 (1988): 54–62. http://dx.doi.org/10.1177/109434208800200406.

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9

Brooks, Stephen P. "Bayesian computation: a statistical revolution." Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 361, no. 1813 (2003): 2681–97. http://dx.doi.org/10.1098/rsta.2003.1263.

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10

Gentle, James E. "Statistical Computation (J. H. Maindonald)." SIAM Review 28, no. 2 (1986): 257–59. http://dx.doi.org/10.1137/1028077.

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11

Kanatani, K. "Statistical Analysis of Geometric Computation." CVGIP: Image Understanding 59, no. 3 (1994): 286–306. http://dx.doi.org/10.1006/ciun.1994.1020.

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12

Kanatani, K. "Statistical Analysis of Geometric Computation." Computer Vision and Image Understanding 59, no. 3 (1994): 286–306. http://dx.doi.org/10.1006/cviu.1994.1024.

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13

Beaumont, Mark A. "Approximate Bayesian Computation." Annual Review of Statistics and Its Application 6, no. 1 (2019): 379–403. http://dx.doi.org/10.1146/annurev-statistics-030718-105212.

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Many of the statistical models that could provide an accurate, interesting, and testable explanation for the structure of a data set turn out to have intractable likelihood functions. The method of approximate Bayesian computation (ABC) has become a popular approach for tackling such models. This review gives an overview of the method and the main issues and challenges that are the subject of current research.

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14

Smolensky, Paul. "Symbolic functions from neural computation." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 370, no. 1971 (2012): 3543–69. http://dx.doi.org/10.1098/rsta.2011.0334.

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Is thought computation over ideas? Turing, and many cognitive scientists since, have assumed so, and formulated computational systems in which meaningful concepts are encoded by symbols which are the objects of computation. Cognition has been carved into parts, each a function defined over such symbols. This paper reports on a research program aimed at computing these symbolic functions without computing over the symbols. Symbols are encoded as patterns of numerical activation over multiple abstract neurons, each neuron simultaneously contributing to the encoding of multiple symbols. Computation is carried out over the numerical activation values of such neurons, which individually have no conceptual meaning. This is massively parallel numerical computation operating within a continuous computational medium. The paper presents an axiomatic framework for such a computational account of cognition, including a number of formal results. Within the framework, a class of recursive symbolic functions can be computed. Formal languages defined by symbolic rewrite rules can also be specified, the subsymbolic computations producing symbolic outputs that simultaneously display central properties of both facets of human language: universal symbolic grammatical competence and statistical, imperfect performance.

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15

Lohr, Sharon, and Ronald A. Thisted. "Elements of Statistical Computing: Numerical Computation." Journal of the American Statistical Association 84, no. 406 (1989): 613. http://dx.doi.org/10.2307/2289953.

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16

Kemp, C. D., and R. A. Thisted. "Elements of Statistical Computing: Numerical Computation." Biometrics 47, no. 1 (1991): 352. http://dx.doi.org/10.2307/2532534.

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17

Steele, Michael. "Elements of Statistical Computing: Numerical Computation." Technometrics 31, no. 4 (1989): 482–83. http://dx.doi.org/10.1080/00401706.1989.10488600.

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18

Scallan, Tony, and Ronald A. Thisted. "Elements of Statistical Computing: Numerical Computation." Statistician 38, no. 2 (1989): 139. http://dx.doi.org/10.2307/2348321.

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19

Machta, J. "Complexity, parallel computation and statistical physics." Complexity 11, no. 5 (2006): 46–64. http://dx.doi.org/10.1002/cplx.20125.

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20

Krutchkoff, Richard G. "Journal of Statistical Computation and Simulation." Wiley Interdisciplinary Reviews: Computational Statistics 1, no. 3 (2009): 365–67. http://dx.doi.org/10.1002/wics.41.

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21

Roy, Urmimala, Tanmoy Pramanik, Subhendu Roy, Avhishek Chatterjee, Leonard F. Register, and Sanjay K. Banerjee. "Machine Learning for Statistical Modeling." ACM Transactions on Design Automation of Electronic Systems 26, no. 3 (2021): 1–17. http://dx.doi.org/10.1145/3440014.

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We propose a methodology to perform process variation-aware device and circuit design using fully physics-based simulations within limited computational resources, without developing a compact model. Machine learning (ML), specifically a support vector regression (SVR) model, has been used. The SVR model has been trained using a dataset of devices simulated a priori, and the accuracy of prediction by the trained SVR model has been demonstrated. To produce a switching time distribution from the trained ML model, we only had to generate the dataset to train and validate the model, which needed ∼500 hours of computation. On the other hand, if 10 6 samples were to be simulated using the same computation resources to generate a switching time distribution from micromagnetic simulations, it would have taken ∼250 days. Spin-transfer-torque random access memory (STTRAM) has been used to demonstrate the method. However, different physical systems may be considered, different ML models can be used for different physical systems and/or different device parameter sets, and similar ends could be achieved by training the ML model using measured device data.

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22

Thiessen, Erik D. "What's statistical about learning? Insights from modelling statistical learning as a set of memory processes." Philosophical Transactions of the Royal Society B: Biological Sciences 372, no. 1711 (2017): 20160056. http://dx.doi.org/10.1098/rstb.2016.0056.

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Statistical learning has been studied in a variety of different tasks, including word segmentation, object identification, category learning, artificial grammar learning and serial reaction time tasks (e.g. Saffran et al. 1996 Science 274 , 1926–1928; Orban et al. 2008 Proceedings of the National Academy of Sciences 105 , 2745–2750; Thiessen & Yee 2010 Child Development 81 , 1287–1303; Saffran 2002 Journal of Memory and Language 47 , 172–196; Misyak & Christiansen 2012 Language Learning 62 , 302–331). The difference among these tasks raises questions about whether they all depend on the same kinds of underlying processes and computations, or whether they are tapping into different underlying mechanisms. Prior theoretical approaches to statistical learning have often tried to explain or model learning in a single task. However, in many cases these approaches appear inadequate to explain performance in multiple tasks. For example, explaining word segmentation via the computation of sequential statistics (such as transitional probability) provides little insight into the nature of sensitivity to regularities among simultaneously presented features. In this article, we will present a formal computational approach that we believe is a good candidate to provide a unifying framework to explore and explain learning in a wide variety of statistical learning tasks. This framework suggests that statistical learning arises from a set of processes that are inherent in memory systems, including activation, interference, integration of information and forgetting (e.g. Perruchet & Vinter 1998 Journal of Memory and Language 39 , 246–263; Thiessen et al. 2013 Psychological Bulletin 139 , 792–814). From this perspective, statistical learning does not involve explicit computation of statistics, but rather the extraction of elements of the input into memory traces, and subsequent integration across those memory traces that emphasize consistent information (Thiessen and Pavlik 2013 Cognitive Science 37 , 310–343). This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences'.

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23

Shenkman, A. L. "Energy loss computation by using statistical techniques." IEEE Transactions on Power Delivery 5, no. 1 (1990): 254–58. http://dx.doi.org/10.1109/61.107281.

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24

Bonin, V., V. Mante, and M. Carandini. "The Statistical Computation Underlying Contrast Gain Control." Journal of Neuroscience 26, no. 23 (2006): 6346–53. http://dx.doi.org/10.1523/jneurosci.0284-06.2006.

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25

Schervish, Mark J. "Applications of Parallel Computation to Statistical Inference." Journal of the American Statistical Association 83, no. 404 (1988): 976–83. http://dx.doi.org/10.1080/01621459.1988.10478688.

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26

Xu, Songgang, and John Keyser. "Statistical geometric computation on tolerances for dimensioning." Computer-Aided Design 70 (January 2016): 193–201. http://dx.doi.org/10.1016/j.cad.2015.06.012.

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27

Zabell, S. L. "Statistical DNA forensics: theory, methods and computation." Law, Probability and Risk 11, no. 1 (2011): 105–10. http://dx.doi.org/10.1093/lpr/mgr017.

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28

Won Lee, J. "Statistical DNA Forensics: Theory, Methods and Computation." Law, Probability and Risk 12, no. 2 (2013): 165. http://dx.doi.org/10.1093/lpr/mgr018.

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29

Briol, François-Xavier, Chris J. Oates, Mark Girolami, Michael A. Osborne, and Dino Sejdinovic. "Probabilistic Integration: A Role in Statistical Computation?" Statistical Science 34, no. 1 (2019): 1–22. http://dx.doi.org/10.1214/18-sts660.

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30

Ramprasath, S., and V. Vasudevan. "Statistical Criticality Computation Using the Circuit Delay." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 33, no. 5 (2014): 717–27. http://dx.doi.org/10.1109/tcad.2013.2296436.

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31

Gadde, Praveen, Sivakumar Arunachalam, and Jitendra Sharan. "The split-mouth investigation and statistical computation." American Journal of Orthodontics and Dentofacial Orthopedics 162, no. 5 (2022): 591–92. http://dx.doi.org/10.1016/j.ajodo.2022.07.017.

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32

Liu, Yafen, Zhen He, Lianjie Shu, and Zhang Wu. "Statistical computation and analyses for attribute events." Computational Statistics & Data Analysis 53, no. 9 (2009): 3412–25. http://dx.doi.org/10.1016/j.csda.2009.02.010.

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33

De Vos, Alexis, and Stijn De Baerdemacker. "The NEGATOR as a Basic Building Block for Quantum Circuits." Open Systems & Information Dynamics 20, no. 01 (2013): 1350004. http://dx.doi.org/10.1142/s1230161213500042.

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Between (classical) reversible computation and quantum computation there exists an intermediate computational world, represented by unitary matrices that have all line sums equal to 1. All of these quantum circuits can be synthesized with the help of merely two building blocks: the NEGATOR and the singly controlled square root of NOT.

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34

MELIK-ALAVERDIAN, VILEN, and M. P. NIGHTINGALE. "QUANTUM MONTE CARLO METHODS IN STATISTICAL MECHANICS." International Journal of Modern Physics C 10, no. 08 (1999): 1409–18. http://dx.doi.org/10.1142/s0129183199001182.

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This paper deals with the optimization of trial states for the computation of dominant eigenvalues of operators and very large matrices. In addition to preliminary results for the energy spectrum of van der Waals clusters, we review results of the application of this method to the computation of relaxation times of independent relaxation modes at the Ising critical point in two dimensions.

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35

Allison, David B., Peter M. Visscher, Guilherme J. M. Rosa, and Christopher I. Amos. "Statistical genetics & statistical genomics: Where biology, epistemology, statistics, and computation collide." Computational Statistics & Data Analysis 53, no. 5 (2009): 1531–34. http://dx.doi.org/10.1016/j.csda.2009.01.005.

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36

Raussendorf, Robert. "Cohomological framework for contextual quantum computations." quantum Information and Computation 19, no. 13&14 (2019): 1141–70. http://dx.doi.org/10.26421/qic19.13-14-4.

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We describe a cohomological framework for measurement-based quantum computation in which symmetry plays a central role. Therein, the essential information about the computation is contained in either of two topological invariants, namely two cohomology groups. One of them applies only to deterministic quantum computations, and the other to general probabilistic ones. Those invariants characterize the computational output, and at the same time witness quantumness in the form of contextuality. In result, they give rise to fundamental algebraic structures underlying quantum computation.

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37

Varley, Thomas F. "A Synergistic Perspective on Multivariate Computation and Causality in Complex Systems." Entropy 26, no. 10 (2024): 883. http://dx.doi.org/10.3390/e26100883.

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What does it mean for a complex system to “compute” or perform “computations”? Intuitively, we can understand complex “computation” as occurring when a system’s state is a function of multiple inputs (potentially including its own past state). Here, we discuss how computational processes in complex systems can be generally studied using the concept of statistical synergy, which is information about an output that can only be learned when the joint state of all inputs is known. Building on prior work, we show that this approach naturally leads to a link between multivariate information theory and topics in causal inference, specifically, the phenomenon of causal colliders. We begin by showing how Berkson’s paradox implies a higher-order, synergistic interaction between multidimensional inputs and outputs. We then discuss how causal structure learning can refine and orient analyses of synergies in empirical data, and when empirical synergies meaningfully reflect computation versus when they may be spurious. We end by proposing that this conceptual link between synergy, causal colliders, and computation can serve as a foundation on which to build a mathematically rich general theory of computation in complex systems.

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38

Heinemann, Florian, Axel Munk, and Yoav Zemel. "Randomized Wasserstein Barycenter Computation: Resampling with Statistical Guarantees." SIAM Journal on Mathematics of Data Science 4, no. 1 (2022): 229–59. http://dx.doi.org/10.1137/20m1385263.

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39

Xu, Yang, and Alain Bernard. "Knowledge Organization Through Statistical Computation: A New Approach." KNOWLEDGE ORGANIZATION 36, no. 4 (2009): 227–39. http://dx.doi.org/10.5771/0943-7444-2009-4-227.

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40

Percy, David F., and P. R. Nelson. "The Frontiers of Statistical Computation, Simulation and Modeling." Journal of the Operational Research Society 43, no. 11 (1992): 1111. http://dx.doi.org/10.2307/2584116.

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41

Kemp, C. D., A. W. Kemp, P. R. Nelson, et al. "The Frontiers of Statistical Computation, Simulation, and Modeling." Biometrics 49, no. 1 (1993): 319. http://dx.doi.org/10.2307/2532631.

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42

Possolo, Antonio. "Statistical models and computation to evaluate measurement uncertainty." Metrologia 51, no. 4 (2014): S228—S236. http://dx.doi.org/10.1088/0026-1394/51/4/s228.

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43

Schils, G. F. "A statistical algorithm for efficient computation of correlations." IEEE Transactions on Signal Processing 40, no. 11 (1992): 2857–63. http://dx.doi.org/10.1109/78.165680.

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44

Percy, David F. "The Frontiers of Statistical Computation, Simulation and Modeling." Journal of the Operational Research Society 43, no. 11 (1992): 1111–12. http://dx.doi.org/10.1057/jors.1992.177.

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45

Kabashima, Yoshiyuki. "Part 5: Statistical mechanics of communication and computation." New Generation Computing 24, no. 4 (2006): 403–20. http://dx.doi.org/10.1007/bf03037401.

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46

Du, K. M., G. Herziger, P. Loosen, and F. Rühl. "Computation of the statistical properties of laser light." Optical and Quantum Electronics 24, no. 9 (1992): S1095—S1108. http://dx.doi.org/10.1007/bf01588608.

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47

Raedt, Luc De, Kristian Kersting, Sriraam Natarajan, and David Poole. "Statistical Relational Artificial Intelligence: Logic, Probability, and Computation." Synthesis Lectures on Artificial Intelligence and Machine Learning 10, no. 2 (2016): 1–189. http://dx.doi.org/10.2200/s00692ed1v01y201601aim032.

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48

Ajayi, Ajibola, Philip Ingrey, Phillip Sewell, and Christos Christopoulos. "Direct Computation of Statistical Variations in Electromagnetic Problems." IEEE Transactions on Electromagnetic Compatibility 50, no. 2 (2008): 325–32. http://dx.doi.org/10.1109/temc.2008.921039.

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49

Briol, François-Xavier, Chris J. Oates, Mark Girolami, Michael A. Osborne, and Dino Sejdinovic. "Rejoinder: Probabilistic Integration: A Role in Statistical Computation?" Statistical Science 34, no. 1 (2019): 38–42. http://dx.doi.org/10.1214/18-sts683.

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50

Adams, N. M., S. P. J. Kirby, P. Harris, and D. B. Clegg. "A review of parallel processing for statistical computation." Statistics and Computing 6, no. 1 (1996): 37–49. http://dx.doi.org/10.1007/bf00161572.

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